Journey

My career has been shaped less by industry and more by structural layers of technology.

I started in embedded systems because I wanted to understand how physical systems behave under constraint. As an undergraduate, I worked with CPLDs, FPGAs, and microcontrollers, building robotic control systems and processing vision data in real time. Embedded engineering forces rigor. Timing budgets, power envelopes, signal integrity, and failure modes are not abstractions. They are observable.

At Applied Materials, that rigor scaled up. I worked on semiconductor fabrication robots operating in atmospheric and vacuum environments, where wafers move between chambers with sub-millimeter precision. Reliability was existential. A small control deviation could cascade into yield loss. Watching process engineers tune plasma parameters and deposition systems sparked a deeper curiosity in me. I wanted to understand not just the robotics around the wafer, but the physics inside the wafer.

That curiosity led me to Stanford, where I studied semiconductor physics and quantum engineering. I wanted to understand how band structures, dopants, and nanoscale geometries translate into device behavior. In parallel, I pursued cryptography. Group theory and abstract algebra felt structurally elegant. Both semiconductor physics and cryptography operate on invisible mathematical structures that determine system behavior at scale.

At Hewlett Packard Enterprise, those threads converged. I worked on hardware security modules, PCIe devices responsible for safeguarding cryptographic keys and executing encryption operations. The challenge was both physical and logical. High-throughput cryptography generates heat, and thermal limits constrain performance. I designed boards under tight envelopes and built a CUDA cluster to run computational fluid dynamics simulations to model airflow and thermal behavior. It was infrastructure in the strictest sense: physics enabling trust.

Bitski was a natural next step. They used HSMs in the backend to protect digital asset signing. I joined because I understood the security primitives. The problem shifted from enterprise hardware to blockchain infrastructure, but the core remained secure key management, deterministic signing, and fault tolerance. It was cryptographic infrastructure abstracted for developers and end users.

During business school, I was exposed more directly to capital flow, asset allocation, and the control levers that determine who has access to capital and who does not. That shifted my perspective. Infrastructure is not only technical. Financial systems are infrastructure as well, and they shape opportunity at a global scale. I began thinking seriously about how programmable systems could broaden access to capital rather than concentrate it.

That motivation led me to Syndicate Protocol. I wanted to work on tools that democratize access to investing and ownership. Blockchain infrastructure made it possible to encode coordination, ownership, and incentives directly into software. At Syndicate, I moved closer to product and go-to-market, translating distributed systems into mechanisms that real communities could use.

At Aineko, we initially built real-time machine learning inference infrastructure. The constraints returned in a new form: latency budgets, streaming data, distributed compute, and production reliability. Real-time ML systems require deterministic pipelines layered over probabilistic models. It felt like embedded systems at cloud scale.

Looking back, the path from robotics to semiconductor physics to cryptography to distributed systems to AI infrastructure is continuous. I have consistently gravitated toward foundational layers where physics, mathematics, and systems design intersect.

Whether it is plasma dynamics in a fabrication chamber, group theory in a cryptographic protocol, airflow in a server chassis, capital coordination in a distributed network, or latency in a streaming inference pipeline, the pattern is the same. Understand the underlying structure. Respect the constraints. Build systems that hold under stress.

That discipline has been the constant.